Aiming at the problems of slow convergence speed and easy to fall into local optimality in the minimum time search(MTS)path programming problem of the maximum and minimum ant colony system(MMAS)algorithm,an improved algorithm based on MMAS is proposed.Firstly,the heuristic function factor is improved combined with the target motion speed.Secondly,pheromones are rewarded for the optimal path.In addition,updated pheromones are used to meet the normal distributed pheromones with adaptive volatilization coefficients,the improved algorithm can speed up the convergence speed of the algorithm and avoid the search falling into local optimum.Simulation results show that the improved ant colony algorithm has a higher probability of searching the target in the search path,and the expected search time is shorter.
关键词
目标搜索/路径规划/信息素表/启发式函数/正态分布
Key words
target search/path planning/pheromones tables/heuristic function/normal distribution
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